Linear dynamic panel data modeling based on linear and nonlinear moment conditions as proposed by Holtz-Eakin, Newey, and Rosen (1988) <doi:10.2307/1913103>, Ahn and Schmidt (1995) <doi:10.1016/0304-4076(94)01641-C>, and Arellano and Bover (1995) <doi:10.1016/0304-4076(94)01642-D>. Estimation of the model parameters relies on the Generalized Method of Moments (GMM) and instrumental variables (IV) estimation, numerical optimization (when nonlinear moment conditions are employed) and the computation of closed form solutions (when estimation is based on linear moment conditions). One-step, two-step and iterated estimation is available. For inference and specification testing, Windmeijer (2005) <doi:10.1016/j.jeconom.2004.02.005> and doubly corrected standard errors (Hwang, Kang, Lee, 2021 <doi:10.1016/j.jeconom.2020.09.010>) are available. Additionally, serial correlation tests, tests for overidentification, and Wald tests are provided. Functions for visualizing panel data structures and modeling results obtained from GMM estimation are also available. The plot methods include functions to plot unbalanced panel structure, coefficient ranges and coefficient paths across GMM iterations (the latter is implemented according to the plot shown in Hansen and Lee, 2021 <doi:10.3982/ECTA16274>). For a more detailed description of the GMM-based functionality, please see Fritsch, Pua, Schnurbus (2021) <doi:10.32614/RJ-2021-035>. For more details on the IV-based estimation routines, see Fritsch, Pua, and Schnurbus (WP, 2024) and Han and Phillips (2010) <doi:10.1017/S026646660909063X>.

An R implementation of the cross-platform, language-independent "port4me" algorithm (<https://github.com/HenrikBengtsson/port4me>), which (1) finds a free Transmission Control Protocol ('TCP') port in [1024,65535] that the user can open, (2) is designed to work in multi-user environments, (3), gives different users, different ports, (4) gives the user the same port over time with high probability, (5) gives different ports for different software tools, and (6) requires no configuration.

Permutation (randomisation) test for single-case phase design data with two phases (e.g., pre- and post-treatment). Correction for dependency of observations is done through stepwise resampling the time series while varying the distance between observations. The required distance 0,1,2,3.. is determined based on repeated dependency testing while stepwise increasing the distance. In preparation: Vroegindeweij et al. "A Permutation distancing test for single-case observational AB phase design data: A Monte Carlo simulation study".

This package provides a central decision in a parametric regression is how to specify the relation between an dependent variable and each explanatory variable. This package provides a semi-parametric tool for comparing different transformations of an explanatory variables in a parametric regression. The functions is relevant in a situation, where you would use a box-cox or Box-Tidwell transformations. In contrast to the classic power-transformations, the methods in this package allows for theoretical driven user input and the possibility to compare with a non-parametric transformation.

Preregistrations, or more generally, registrations, enable explicit timestamped and (often but not necessarily publicly) frozen documentation of plans and expectations as well as decisions and justifications. In research, preregistrations are commonly used to clearly document plans and facilitate justifications of deviations from those plans, as well as decreasing the effects of publication bias by enabling identification of research that was conducted but not published. Like reporting guidelines, (pre)registration forms often have specific structures that facilitate systematic reporting of important items. The preregr package facilitates specifying (pre)registrations in R and exporting them to a human-readable format (using R Markdown partials or exporting to an HTML file) as well as human-readable embedded data (using JSON'), as well as importing such exported (pre)registration specifications from such embedded JSON'.

Create an interactive pizza chart visualizing a specific player's statistics across various attributes in a sports dataset. The chart is constructed based on input parameters: data', a dataframe containing player data for any sports; player_stats_col', a vector specifying the names of the columns from the dataframe that will be used to create slices in the pizza chart, with statistics ranging between 0 and 100; name_col', specifying the name of the column in the dataframe that contains the player names; and player_name', representing the specific player whose statistics will be visualized in the chart, serving as the chart title.

This package provides a general framework for constructing partial dependence (i.e., marginal effect) plots from various types machine learning models in R.

The population proportion using group testing can be estimated by different methods. Four functions including p.mle(), p.gart(), p.burrow() and p.order() are provided to implement four estimating methods including the maximum likelihood estimate, Gart's estimate, Burrow's estimate, and order statistic estimate.

Data and examples from meta-analyses in psychology research.

The Prais-Winsten estimator (Prais & Winsten, 1954) takes into account AR(1) serial correlation of the errors in a linear regression model. The procedure recursively estimates the coefficients and the error autocorrelation of the specified model until sufficient convergence of the AR(1) coefficient is attained.

Markov chain Monte Carlo diagnostic plots. The purpose of the package is to combine existing tools from the coda and lattice packages, and make it easy to adjust graphical details.

Utility functions for the handling, analysis and visualisation of data from portable emissions measurement systems ('PEMS') and other similar mobile activity monitoring devices. The package includes a dedicated pems data class that manages many of the quality control, unit handling and data archiving issues that can hinder efforts to standardise PEMS research.

This package performs Bayesian arm-based network meta-analysis for datasets with binary, continuous, and count outcomes (Zhang et al., 2014 <doi:10.1177/1740774513498322>; Lin et al., 2017 <doi:10.18637/jss.v080.i05>).

This package implements recently developed projection pursuit algorithms for finding optimal linear cluster separators. The clustering algorithms use optimal hyperplane separators based on minimum density, Pavlidis et. al (2016) <http://jmlr.org/papers/volume17/15-307/15-307.pdf>; minimum normalised cut, Hofmeyr (2017) <doi:10.1109/TPAMI.2016.2609929>; and maximum variance ratio clusterability, Hofmeyr and Pavlidis (2015) <doi:10.1109/SSCI.2015.116>.

Generates Weibull-parameterized estimates of phenology for any percentile of a distribution using the framework established in Cooke (1979) <doi:10.1093/biomet/66.2.367>. Extensive testing against other estimators suggest the weib_percentile() function is especially useful in generating more accurate and less biased estimates of onset and offset (Belitz et al. 2020) <doi:10.1111/2041-210X.13448>. Non-parametric bootstrapping can be used to generate confidence intervals around those estimates, although this is computationally expensive. Additionally, this package offers an easy way to perform non-parametric bootstrapping to generate confidence intervals for quantile estimates, mean estimates, or any statistical function of interest.

This package provides a customisable R shiny app for immersively visualising, mapping and annotating panospheric (360 degree) imagery. The flexible interface allows annotation of any geocoded images using up to 4 user specified dropdown menus. The app uses leaflet to render maps that display the geo-locations of images and panellum <https://pannellum.org/>, a lightweight panorama viewer for the web, to render images in virtual 360 degree viewing mode. Key functions include the ability to draw on & export parts of 360 images for downstream applications. Users can also draw polygons and points on map imagery related to the panoramic images and export them for further analysis. Downstream applications include using annotations to train Artificial Intelligence/Machine Learning (AI/ML) models and geospatial modelling and analysis of camera based survey data.

Anomaly detection method based on the paper "Truth will out: Departure-based process-level detection of stealthy attacks on control systems" from Wissam Aoudi, Mikel Iturbe, and Magnus Almgren (2018) <DOI:10.1145/3243734.3243781>. Also referred to the following implementation: <https://github.com/rahulrajpl/PyPASAD>.

Monte Carlo based model choice for applied phylogenetics of continuous traits. Method described in Carl Boettiger, Graham Coop, Peter Ralph (2012) Is your phylogeny informative? Measuring the power of comparative methods, Evolution 66 (7) 2240-51. <doi:10.1111/j.1558-5646.2011.01574.x>.

Using the Bayesian state-space approach, we developed a continuous development model to quantify dynamic incremental changes in the response variable. While the model was originally developed for daily changes in forest green-up, the model can be used to predict any similar process. The CDM can capture both timing and rate of nonlinear processes. Unlike statics methods, which aggregate variations into a single metric, our dynamic model tracks the changing impacts over time. The CDM accommodates nonlinear responses to variation in predictors, which changes throughout development.

An R-package-version of an open online science-based personality test from <https://openpsychometrics.org/tests/IPIP-BFFM/>, providing a better-designed interface and a more detailed report. The core command launch_test() opens a personality test in your browser, and generates a report after you click "Submit". In this report, your results are compared with other people's, to show what these results mean. Other people's data is from <https://openpsychometrics.org/_rawdata/BIG5.zip>.

This package provides a Boolean network is a particular kind of discrete dynamical system where the variables are simple binary switches. Despite its simplicity, Boolean network modeling has been a successful method to describe the behavioral pattern of various phenomena. Applying stochastic noise to Boolean networks is a useful approach for representing the effects of various perturbing stimuli on complex systems. A number of methods have been developed to control noise effects on Boolean networks using parameters integrated into the update rules. This package provides functions to examine three such methods: Boolean network with perturbations (BNp), described by Trairatphisan et al. (2013) <doi:10.1186/1478-811X-11-46>, stochastic discrete dynamical systems (SDDS), proposed by Murrugarra et al. (2012) <doi:10.1186/1687-4153-2012-5>, and Boolean network with probabilistic edge weights (PEW), presented by Deritei et al. (2022) <doi:10.1371/journal.pcbi.1010536>. This package includes source code derived from the BoolNet package, which is licensed under the Artistic License 2.0.

Generates a position balanced or nearly position balanced block design with given parameters. This package can also convert a given proper and equireplicate block design into a position balanced or nearly position balanced block design.

Package for learning and evaluating (subgroup) policies via doubly robust loss functions. Policy learning methods include doubly robust blip/conditional average treatment effect learning and sequential policy tree learning. Methods for (subgroup) policy evaluation include doubly robust cross-fitting and online estimation/sequential validation. See Nordland and Holst (2022) <doi:10.48550/arXiv.2212.02335> for documentation and references.

Partial Least Squares Path Modeling (PLS-PM), Tenenhaus, Esposito Vinzi, Chatelin, Lauro (2005) <doi:10.1016/j.csda.2004.03.005>, analysis for both metric and non-metric data, as well as REBUS analysis, Esposito Vinzi, Trinchera, Squillacciotti, and Tenenhaus (2008) <doi:10.1002/asmb.728>.